The Selected Factors Related with Mental Health Power of Nursing Undergraduate Students, Srimahasarakham Nursing College, Praboromarajhanok Institute of Thailand
Bibliographic record
Abstract
The purposes of this research were to study the level of mental health power and selected factors related to mental health power of nursing undergraduate students, Srimahamarakham Nursing College. The sample consisted of 91 first-year nursing undergraduate students at Srimahasarakham Nursing College, Faculty of Nursing, Praboromarajhanok Institute who were selected by purposive sampling. The research tool was the mental health power assessment questionnaire. The statistics used for data analysis were frequency, percentage, mean, standard deviation and the spearman's correlation coefficient. The results showed that the mean of overall mental health power was lower than the criteria (x ̅ = 57.64). When considering mental health power on each aspect, it was found that emotional stability x ̅ = 28.98, encouragement x ̅ = 13.81 and problem management x ̅ = 14.84 which is below the threshold level on all aspects. And factors that had a positive correlation with the mental health power of nursing undergraduate students statistical significance (p< .05). There was also a moderate correlation (r = .04) were associated homeland low level (r = .01) and monthly income had a moderate relationship (r = .03). The factors that did not relate with the mental health power of nursing undergraduate students were gender (r = .10) and the order of siblings or their children (r = .09).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".